CN1795132A - Identification of incoming peak traffic for elevators - Google Patents
Identification of incoming peak traffic for elevators Download PDFInfo
- Publication number
- CN1795132A CN1795132A CN200480014443.0A CN200480014443A CN1795132A CN 1795132 A CN1795132 A CN 1795132A CN 200480014443 A CN200480014443 A CN 200480014443A CN 1795132 A CN1795132 A CN 1795132A
- Authority
- CN
- China
- Prior art keywords
- peak
- elevator
- traffic situation
- peak traffic
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/02—Control systems without regulation, i.e. without retroactive action
- B66B1/06—Control systems without regulation, i.e. without retroactive action electric
- B66B1/14—Control systems without regulation, i.e. without retroactive action electric with devices, e.g. push-buttons, for indirect control of movements
- B66B1/18—Control systems without regulation, i.e. without retroactive action electric with devices, e.g. push-buttons, for indirect control of movements with means for storing pulses controlling the movements of several cars or cages
- B66B1/20—Control systems without regulation, i.e. without retroactive action electric with devices, e.g. push-buttons, for indirect control of movements with means for storing pulses controlling the movements of several cars or cages and for varying the manner of operation to suit particular traffic conditions, e.g. "one-way rush-hour traffic"
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Elevator Control (AREA)
- Indicating And Signalling Devices For Elevators (AREA)
Abstract
The present invention relates to a method whereby the service capacity of the elevator system of a building can be improved by effectively identifying an incoming peak traffic situation. When the number of elevator passengers arriving to the lobby floor of the building exceeds a given peak traffic threshold value, the elevators serving the passengers are directed after the trip back to the lobby floors without a separate call. To allow faster detection of a peak traffic condition, use is made of both information obtained from traditional peak hour identification and history data obtained from statistics regarding the numbers of passengers.
Description
Technical field
The present invention relates to the control of eleva-tor bank.
Background technology
When the passenger wishes to take a lift when advancing, he calls out elevator by pressing ascending stair (landing) call button that is installed in the floor place.The control system of elevator receives this calling, and which elevator of attempting in definite eleva-tor bank can be served for the people who sends calling.This behavior is call distribution.The problem that distribution will solve is to select to make the minimized elevator of selecting in advance of cost function (cost function) for per call.
Elevator group control system generally is configured to control elevator according to the control algorithm of selecting in advance.Selected control algorithm depends on dominant traffic (traffic) type in building at that time.Therefore, elevator group control system usually comprises traffic type detec-tor.By the traffic pattern of basic traffic type detec-tor identification is for example " normal traffic ", " entering (incoming) peak traffic ", " (outgoing) peak traffic of going out ", " two-way peak traffic ".Particularly important to the quick and reliable detection that enters peak traffic condition.In office building, entering peak traffic condition may occur during a few minutes when people arrive its work place at short notice in the morning.Fig. 1 has presented the example that typical case in the office building enters traffic.
During entering peak traffic, the radical function that set control system will be finished is in the proper ratio elevator to be back to the inlet floor.If in the normal traffic operational mode, return an elevator for each calling of being sent, entering under the state of traffic peak so, make elevator directly turn back to the inlet floor, and need not independent calling, till system determines that peak traffic condition no longer exists.Because typically have only a landing calls at inlet on the floor, normally up call is effective, so the distribution decision that the operation of system can not be subjected to make based on landing calls influences.If during entering peak traffic, do not activate directly returning of elevator, such situation will take place so, promptly only two elevators of each inlet floor will move; One one has carried the passenger and they has been transported to their destination floor, and another one is empty and return the inlet floor according to the calling of sending from the inlet floor.If do not identify rapidly and enter peak traffic, will form the queue row on the inlet floor in building the hall or usually so, and passenger's wait time will become longer.High latency may cause being discontented with the elevator operation.
On the other hand, be that strong measures and its will disturb other elevator service in the building greatly without the activation that requires (uncalled-for) because elevator directly turns back to the inlet floor, should unnecessarily do not activated so enter the peak pattern.In this case, for the call service sent from inlet other floor outside the floor obviously than during normal traffic slowly many.The algorithm that returns of control elevator must so be designed, and, during entering the peak traffic situation for a long time, although delay is arranged, will be the call service of sending from other floor that is.
The identification that enters peak traffic condition is comprised two targets that part is opposite.Identification must be worked as quickly as possible, but it can not produce the off-square recognition result.
In to the tional identification that enters peak traffic, when the passenger enters elevator in the lobby area (in this case, this comprises each inlet floor of building), the number that monitoring is called out.In these were called out, special consideration had the number of the calling of the destination outside lobby area.When number of calls surpassed predetermined threshold value, the elevator of being discussed was considered to peak elevator, and this situation is considered to the potential peak traffic condition that enters.
The threshold value of corresponding types also is set for car load.When elevator left lobby area and its load and surpasses threshold value, this elevator was considered to peak elevator, and this situation is considered to the potential peak traffic condition that enters.When in the specified time window, detecting two ones or more peak elevator, activate the peak traffic pattern that enters, itself so start elevator directly returning to the inlet floor.Two peak elevator that need place of the given schedule time will be not can unnecessarily not take place according to the peak elevator of the chance of actual peak traffic outside the time to guarantee peak time (hour) identification.On the other hand, this hinders the early stage identification to the real peak traffic situation at the real peak traffic state.
At the peak traffic time durations of reality, if activated the peak traffic pattern that enters according to the single peak elevator of discerning, this will be favourable so.For this purpose, be provided with two independent time windows the peak time that can typically be morning and lunchtime in control system, during described peak time, the identification of single peak elevator is enough to be used in entering the activation of peak traffic pattern.The problem of this solution is to be necessary that the elevator of understanding building and its user well enough utilizes the time, so that can aforementioned time window be set in the most probable peak traffic condition time opening.In addition, because compare the use overview of building elevator during weekend with working day generally different, so the possibility that time window is set separately every day at a week preferably should be arranged.Working day is closely similar each other once more.Yet, in fact because the control logic of elevator device generally only allow to be provided with two regular time window, so time window can not be set separately for the every day in a week.
The passenger's of each floor that arrives and leave building number is calculated in the identification based on the travel forecasting device (TF) of peak traffic condition, and keep the statistical figure (statistics) of these numbers.This calculating is just to rest on this floor and the passenger is just leaving and the time durations that enters car carries out at elevator.This calculating is to use to the basis with car load weighing-appliance and the optical element (light cell) that provides in elevator door.
Collect the statistical figure of two kinds dissimilar---long-time statistical (LTS) and short-term statistics (STS)---based on the identification in peak time of TF.It is for example " the passenger's numbers in 15 minutes " that the unit that uses in the LTS statistical figure measures, and is " the passenger's numbers in 5 minutes " in the STS statistical figure.
Produce the LTS statistical figure at each floor i.For each floor i, there are four traffic component k: from the passenger of following this floor of arrival, from the passenger of top this floor of arrival, the passenger that leaves the passenger of this floor and leave this floor in the upward direction in a downward direction.In the LTS statistical figure, be divided into each 96 time slice t of 15 minutes with one day: first time of covering from 00:00 to 00:15, following a slice is from 00:15 to 00:30, and last a slice is from 23:45 to 00:00.Therefore, the LTS statistical figure are three-dimensional matrice L
I, k, tBy day, the passenger is collected into statistical figure L every day
I, k, t *At midnight, statistical figure are subjected to adding up Acceptance Test between collected daytime, are not red-letter days of midweek for example so that guarantee collected that day.If statistical figure have passed through Acceptance Test between daytime, the LTS statistical figure will be updated e.g. as follows so:
L
i，k，t＝(1-α)·L
i，k，t+α·L
i，k，t * (1)
Wherein, α upgrades the factor (0＜α＜1).Selected α value less usually (0.1...0.2).For representative type α value, this method keeps most of legacy data and adds some new datas.According to school, this update method is called as exponential equalization or linear IIR (IIR, infinite impulse response) LPF.Equation (1) is created in the unsteady aviation value (floating average) of the traffic component k of the floor i of building during the time slice t.Its describes situation in the past, and in other words, it is given in during the time slice t that is discussed the passenger's who moved before on the floor i average number.
The floor that comprises in the lobby area of building is known, can be from LTS statistical figure generation traffic overview as shown in fig. 1.By making traffic component proportional and use fuzzy logic, can discern even have the different traffic patterns of very trickle difference with the transport capacity of the eleva-tor bank calculated.US Patent 5,229,559 have described the such method that is used for determining traffic pattern on the basis of statistics.Yet in fact, the LTS statistical figure can not be directly used in determines dominant traffic pattern in building, because the LTS statistical figure are represented the long-term average of past observed traffic in building.Considered the time be engraved in the building actual take place may be greatly different with long-term average.Therefore, the traffic pattern that obtains from the LTS statistical figure should be considered to indication at each time instant dominant traffic pattern building usually.
In the trial that solves the above problems, short-term STS statistical figure have been introduced.STS statistical figure and LTS statistical figure difference are: they form two-dimensional matrix S
I, k, wherein i represents that floor and k represent traffic component.Because calculate passenger's number with ralocatable mode in the five-minute period before current time and it be included in the STS statistical figure, so time index t disappears.In other words, used the passenger of elevator from statistical figure, to be removed in five minutes in the past.For discern current in building dominant traffic pattern, the STS statistical figure are subjected to the above-mentioned fuzzy logic derivation identical with the LTS statistical figure.
After this, green phase ought complicated a series of deductions make up the information that is included in LTS and the STS statistical figure.About this point, will compare mutually by the traffic pattern that statistical figure provide, will compare by rate of traffic flow and the system's transport capacity that STS measures, and utilize the LTS statistical figure to obtain affirmation the traffic pattern that provides by the STS statistical figure.
There are two principle problems relevant with the method.The first, because the length difference in the cycle of being considered: in LTS, be typically 15 minutes and be 5 minutes in STS, so LTS and STS statistical figure can not compare mutually.In addition, the time slice in the LTS statistical figure be fix and have 15 minutes constant length, and in the STS statistical figure, infinitely float in time window circulated between whole daytime.The second, particularly consider to enter peak traffic, five minutes time window of STS statistical figure for be used for activating enter for the pattern of peak still oversize.
The 3rd problem is relevant with actual realization.The complicated derivation that is used to make up the traffic pattern that is produced by STS and LTS needs the many threshold values of independent adjustment.In addition, finishing (trim) and test order group itself are difficult tasks.
Goal of the invention
The objective of the invention is to overcome above-mentioned shortcoming or it is alleviated greatly.Specific purpose of the present invention be to realize than in the past faster and more failure-free to entering the identification of peak traffic condition.For feature of the present invention, with reference to claim.
Summary of the invention
The present invention discloses a kind of being used for and enters method, computer program and the system of peak traffic condition in elevator device identification.
The present invention will be combined with the real time information that discerns acquisition from tradition peak time from the information that statistical figure obtain.Long-time at interval in LTS (long-time statistical) statistics of collection be illustrated in the elevator of the building of being considered in the observed passenger flows of one day different time.Usually, form formation in the morning and about lunch break finishes at the lobby floor place.According to statistical figure, can distinguish on lobby floor beginning and form the congested most probable time gradually.In the control of traditional elevator, to serve by an elevator by pressing the calling that call button provides, it is motionless that this elevator keeps behind this trip, thereby wait for calling next time.This method is worked in the peak traffic situation unhandily.Service is slow and the client is dissatisfied.Need a kind of algorithm of exploitation, it will allow to detect quickly and enter peak traffic condition, and permit activating elevator to directly the returning of lobby floor, and need not to press separately call button.
The application of the invention can realize entering the faster identification of peak traffic condition.In an embodiment of the present invention, utilize statistical figure to determine the potential peak time that lobby floor gets clogged usually.Simultaneously, by tradition monitoring to car call and car load, the elevator in the Real Time Observation elevator device, and when surpassing given threshold value, the peak elevator state is assigned to this elevator.Threshold value for example is meant the total weight or the number of elevator passenger.In addition, in this embodiment, a peak elevator has been enough to activation and has entered the peak traffic pattern, and promptly elevator directly returns.
In another embodiment of the present invention, predict the number that accumulates in the passenger on the lobby floor by the so-called theoretical time gap that utilizes statistical figure and elevator to leave between time of lobby floor.If the client that prediction provides outnumbers the car load threshold value that is used for tradition identification in peak time, then this situation is considered as potential peak time, in this case, even a peak elevator that is detected just is enough to activate directly returning of elevator.
As the extension of basic thought of the present invention, can also in prediction, comprise time window before the time window that is used for moment of being considered and/or time window thereafter.In this case, when learning that begin peak time on the basis at statistical figure, this method is taked " prediction (lookahead) " to future to a certain extent, and quickens entering the identification of peak traffic condition.
The present invention has several advantages as com-pared with prior art.Realized that to entering the quick identification of peak traffic condition as its result, the activation that begins in peak time enters the peak traffic pattern, compared that the formation in the hall is shorter with tradition identification in peak time.In this way, provide better service and make the passenger more satisfied.During the peak time of statistic record, system identifies peak traffic condition according to single peak elevator.In best situation,, also can activate the peak traffic pattern that enters by the deduction of making from a large amount of car call even when only loading first peak elevator at the lobby floor place.
Second significant advantage of the present invention is to have realized entering the reliable recognition of peak traffic condition.This system also outside not by the peak time of statistic record, according to two peak elevator, reasonably identifying " unforeseen " peak traffic condition in the time.After initial start (during about several weeks), elevator device can not utilize the LTS statistical figure because this system still the off-duty long enough time to allow collection to statistical figure.In this case, under not by the situation of the statistical figure on the conventional principle, realize best identification in peak time, only after detecting two peak elevator, just activate identification in peak time thus.
The 3rd advantage of the present invention is that this function can be automation.Collected statistical figure are every day distinctive (day-specific), and the traffic overview of statistic record that specifically is used for weekend is obviously with to be used for workaday corresponding overview different.If manually be provided with potential peak time, then they are actvies in the every day in a week, at one day identical time durations, and can not make amendment so that they are that every day is distinctive to it.Nature, this is a shortcoming undoubtedly.In addition, for circulating between daytime, can be set maximum only two potential peak times that manually are provided with usually.In addition, statistical figure can comprise not limited number potential peak time on principle.And recognition function comprises the adaptive great advantages relevant with availability automatically.If great change has taken place in the traffic situation in building, these changes will appear in the LTS statistical figure in the near future so, so peak time, identification was always adapted to dominant passenger's behavior.
In addition, elevator device can be simplified by the following fact transporting of client: use new recognition methods in peak time, can abandon will be when transporting or two parameters of configuration at the scene.
Description of drawings
Fig. 1 presents the example that typical case in the office building enters traffic situation,
Fig. 2 presents the block scheme of representing method of the present invention, and
Fig. 3 presents the example of the system that uses method of the present invention.
The specific embodiment
Fig. 2 presents the diagram of circuit of the operation of representing method of the present invention.Discern in 14 peak time in tradition, can come to detect fast and reliably peak elevator by means of detector.' detector ' be meant car load weighing-appliance or elevator optical element or they the two.In best situation, when elevator still when admitting the passenger, detect peak elevator according to the number of car call 11.When in the specified time window, detecting two peak elevator, activate the peak traffic pattern 17 that enters.Yet if tional identification receives the information in advance about potential peak time, its place of working is better so.Building and the people's that advances therein traffic behavior is known, usually can at the scene manually be input to control system peak time.On the other hand, TF (travel forecasting device) statistical figure and LTS statistical figure (long-time statistical numeral) 12 comprise the conventional peak time just and discern 14 these required information.Conventional peak time recognition system detects current situation about taking place in building, and TF and LTS statistical figure disclose situation about taking place usually at this building this moment.
In the embodiment of Fig. 2, if the traffic pattern that is provided by LTS statistical figure 12 in 15 minutes the time slice in the moment of considering comprising is that for example ' heavy_incoming ' or ' intense_incoming ' is (common, for example at 07.45-08.00 between o'clock), tradition is discerned 14 and activated the peak traffic pattern that enters peak time when being detected single peak elevator so.During other traffic pattern that provides by the LTS statistical figure, need two elevators to activate the peak traffic pattern that enters.These traffic patterns for example comprise normal traffic, enter peak traffic, go out peak traffic and two-way peak traffic.
In another embodiment of Fig. 2, in piece 13, be eleva-tor bank theory of computation time gap t
1In entering the situation of peak traffic condition, this is meant the average time interval between the setting out of the elevator that leaves lobby floor.According to the LTS statistical figure, the passenger's who on lobby floor, assembles during (that is, the passenger accumulates in the time gap in the formation of the elevator of waiting for that next one arrives) between prediction at this moment number n
P
n
P＝t
I·(L
i，up＞，t+L
i，dn＞，t) (2)
Wherein i is the subscript of lobby floor, and up＞and dn＞be meant is from being orientated the subscript of the traffic component 10 of leaving this floor, and t is the subscript of current 15 minutes time slice.If passenger's number n of prediction
PSurpass the predetermined car load threshold of tradition identification in peak time, this situation will be considered to potential peak time so.In the case, peak elevator is enough to be used in identification and enters peak traffic.Otherwise, need two peak elevator.
The foregoing description part that differs from one another especially is: in the embodiment of back, can omit the fuzzy logic of carrying out according to the LTS statistical figure and derive.In above-mentioned two embodiment,, use the traffic pattern 16 that provides by STS 15 so if conventional traffic detector 14 provides the traffic pattern except entering peak traffic.This is chosen in the piece 17 and carries out.
In identification, except 15 minutes time window, in processing, can comprise or even fwd time window (having subscript ' T-1 ') and next time window (having subscript ' T+1 ') potential peak traffic condition.In this case, accumulating in the number of the passenger in the elevator formation can be predicted as follows:
n
P1＝t
I·(L
i，up＞，t-1+L
i，dn＞，t-1)·β
n
P2＝t
I·(L
i，up＞，t+L
i，dn＞，t) (3)
n
P3=t
I(L
I, up＞, t+1+ L
I, dn＞, t+1) wherein β and χ are configuration coefficients (0≤β≤1 and 0≤χ≤1) to χ.If the queue length n that is calculated
P1, n
P2Or n
P3One of surpass the car load threshold value, this situation can be considered to potential peak time so, thus, infers the conversion that enters the peak traffic pattern once more as mentioned above.This consideration is based on expect future event by the next time window of looking forward to the prospect.But if represent the peak time current time still in normal traffic in the time according to the next time window of statistical figure, can think so the detected peak elevator indication of current time enter peak traffic condition to begin be very possible.Can carry out cooresponding deduction from the time window before the current time.If represent to enter peak traffic condition, mean still that in the detected peak elevator of current time the actual peak traffic situation that enters is very possible so according to statistical figure fwd time window.Can use configuration coefficients β and χ to adjust the sensitivity of ' prediction '.
In eleva-tor bank, such situation often takes place, wherein, all elevators in described group all are not normal passenger's transport services.Elevator may safeguard that they may be the special calling service or be used for some other specific purposes.In these cases, the transport capacity of all the other eleva-tor bank reduces, and subnormal absolute rate of traffic flow causes the peak traffic situation.When elevator disappears from the service of normal traffic, time gap t
IIncrease.Therefore, according to (2) and (3), n
PIncrease, conclude faster arrival car load threshold value thus once more.Because peak time, recognition system was transformed into potential peak traffic pattern under subnormal rate of traffic flow, so the transport capacity of the eleva-tor bank that is reduced is considered automatically.
Fig. 3 has presented the example that can use the system of method of the present invention.In this example, elevator device comprises two elevators 20,23.Elevator is provided with optical element 22,25 and car load weighing- appliance 21,24, is used for real-time monitor passenger number.Be imported into control logic 26 about the ridership destination data, the motion of the elevator in control logic 26 in the control elevator device.Be stored in the data bank 27 about ridership purpose statistics by elevator traffic.Except top described, it is to make a determination from the most typical traffic pattern at the moment of being considered that statistical figure obtain that control logic also is used for to which.In addition, based on method of the present invention, control logic is made the judgement about dominant traffic pattern, and controls elevator according to the judgement of so making.In other words, if surpassed car load threshold value that is used for identification in peak time and the statistics indication peak traffic situation of collecting for the current time window at least one elevator, control logic thinks that dominant traffic pattern is a peak traffic condition so.In fact, control logic is by for example constituting with realizing the computing machine about the computer program of the control of the judgement of traffic pattern and elevator.
In the embodiments of figure 3, this system comprises: first determines parts, be used for according to the user number, on the basis of statistics, determine value of weighing (weighting value) of inlet floor; And function unit, be used for during entering the peak traffic situation, elevator be directed to the inlet floor according to the value of so determining of weighing.
In the embodiments of figure 3, this system comprises that second determines parts, is used for determining the real-time peak traffic situation of identification number required, simultaneous peak elevator.
In the embodiments of figure 3, this system comprises: the 3rd determines parts, is used for determining the length of the time window that will use in statistics; Calculating unit is used for respect to the number that arrives and leave the passenger of floor in the determined time window of one day Time Calculation; Summation component, be used for at circulating collection between the daytime of being considered and comprise that the described statistics of ridership purpose is added to the existing statistics with the weighting of scheduled update coefficient; And the first derivation parts, be used at the dominant most probable traffic pattern during each time window of deriving on the basis of described statistics.
In the embodiments of figure 3, this system comprises: first identification component, if be used for aforesaid statistical data indication peak traffic situation, then discern potential peak traffic situation; And the second derivation parts 26, be at least 1 still less than the number of aforementioned simultaneous peak elevator if be used for the number of detected peak elevator during potential peak traffic situation, think that so this potential peak traffic situation is actual peak traffic condition.
In the embodiments of figure 3, this system comprises: time gap is determined parts, is used to calculate elevator and leaves the average time interval that enters the mouth between the floor; Estimation section is used for predicting the number that accumulates in the passenger of elevator formation during aforementioned time gap according to statistics; First identification component is used for discerning potential peak traffic situation when the aforementioned passenger who predicts outnumbers the car load threshold value that is used for identification in peak time; And the second derivation parts, if be used for the number of detected peak elevator during potential peak traffic situation be at least 1 but less than the number of aforementioned simultaneous peak elevator, infer that then this potential peak traffic situation is actual peak traffic situation.
In the embodiments of figure 3, the second derivation parts are arranged to: be used to discern outside the potential peak traffic situation of actual peak traffic situation, needing the peak elevator of foregoing at least number.
In the embodiments of figure 3, this system comprises: the 4th determines parts, is used for determining before the time window that statistics is used and the coefficient of weight of one or more time windows afterwards; Estimation section is the time window in the moment that is used for considering except being used for, for all aforementioned time windows, by using the number of predicting the passenger of accumulation with aforementioned manner of determined coefficient of weight; Second identification component if at least one that is used for aforementioned passenger's number of predicting surpasses the car load threshold value be used for identification in peak time, then identifies potential peak traffic situation; And the second derivation parts, if to be used for the number of detected peak elevator during potential peak traffic situation be 1 at least but less than the number of aforementioned simultaneous peak elevator, infer that then this potential peak traffic situation is actual peak traffic situation.
Above-mentioned parts for example use, and control logic 26 realizes.These parts also can be implemented as the combination of software and hardware.
The recognition principle of operating in the above described manner in peak time can be likened to the automatic stop (parking) of elevator.Usually, when elevator transports, manually determine to stop floor, perhaps situ configuration they.In automatically stopping, according to being orientated the traffic component of leaving floor, on the basis of LTS statistical figure, building being divided into and stopping the zone.In each zone, select to have the floor of the most intensive traffic of leaving this floor as the main floor of stopping.Define these zones once more by this way, promptly in each zone, the total rate of traffic flow that leaves the floor of zones of different equates.Therefore, compare with the floor with intense traffic, the floor with quiet traffic forms higher zone.According to carrying out elevator to the actual assignment of stopping floor with mode identical in the situation of the floor of manual definition.
According to mode corresponding to above-mentioned automatic stop, wherein, use statistical figure to determine where elevator preferably should rest in and carry out actual stop by orthodox method, in identification in peak time, in piece 103, read statistical figure, should when expect the potential peak traffic situation that enters to check, and in piece 14, discern the actual peak traffic condition that enters by orthodox method.Therefore, statistical figure have they prevailing effects.They serve as the subsidiary of actual judgement, and reality judges that basis is about carrying out in the information of current time actual event in system again.
The invention is not restricted to the foregoing description example; But, in the scope of the present invention of definition design, might carry out many variations in the claims.
Claims (27)
1. one kind is used for discerning the method that enters peak traffic condition at elevator device, it is characterized in that this method may further comprise the steps:
In the identification in real-time peak time of elevator device, the number of monitoring car call and the car load of admitting the elevator of the passenger in the lobby area;
Determine the car load threshold value,, then elevator is identified as peak elevator if car load surpasses the car load threshold value on its basis;
The threshold value of definition car call is if the threshold value that outnumbers car call of the car call of the floor on its basis outside the lobby area then identifies peak elevator;
In elevator device, collect about passenger's number of arrival floor during the window at the fixed time and leave the ridership purpose statistics of this floor; And
If detect at least one peak elevator and enter peak traffic condition, then select dominant traffic pattern as entering peak traffic condition for the statistics indication that the current time window is collected.
2. method according to claim 1 is characterized in that this method is further comprising the steps of:
Determine the number of the simultaneous peak elevator that the real-time peak traffic situation of identification is required.
3. method according to claim 2 is characterized in that this method is further comprising the steps of:
The number of aforementioned simultaneous peak elevator is chosen as 2.
4. method according to claim 1 is characterized in that this method is further comprising the steps of:
On the basis of statistics and according to passenger's number determine the inlet floor the value of weighing; And
According to the value of determining like this of weighing, during entering peak traffic condition, elevator is directed to the inlet floor.
5. method according to claim 1 is characterized in that this method is further comprising the steps of:
The length of the time window that definition will be used in statistics;
With respect to one day time, calculate the number that in defined time window, arrives and leave the passenger of this floor;
Will at circulating collection between the daytime of being considered, be added on the existing statistics of utilizing the weighting of scheduled update coefficient about described ridership purpose statistics; And
Infer dominant during each time window, most probable traffic pattern from described statistics.
6. according to claim 2 or 3 described methods, it is characterized in that this method is further comprising the steps of:
If described statistics indication peak traffic situation then identifies potential peak traffic situation; And
If the number of detected peak elevator is at least 1 still less than the number of aforementioned simultaneous peak elevator during potential peak traffic situation, think that then this potential peak traffic situation is actual peak traffic situation.
7. according to claim 2 or 3 described methods, it is characterized in that this method is further comprising the steps of:
Calculate elevator and leave the described time gap that enters the mouth between the floor;
According to statistics, prediction accumulates in the number of the passenger in the elevator formation during aforementioned time gap;
When the aforementioned passenger who predicts outnumbers the car load threshold value that is used for identification in peak time, identify potential peak traffic situation; And
If the number of detected peak elevator is at least 1 still less than the number of aforementioned simultaneous peak elevator during potential peak traffic situation, infer that then this potential peak traffic situation is actual peak traffic situation.
8. according to claim 6 or 7 described methods, it is characterized in that this method is further comprising the steps of:
Be used to discern outside the potential peak traffic situation of actual potential peak traffic situation, needing the peak elevator of described at least simultaneous number.
9. method according to claim 7 is characterized in that this method is further comprising the steps of:
For before the time window that in statistics, uses and one or more time windows afterwards determine coefficient of weight;
The time window in the moment of considering except being used for, for all aforementioned time windows, by using the number of predicting the passenger who assembles with aforementioned manner of determined coefficient of weight;
If at least one in described passenger's number of predicting surpasses the car load threshold value that is used for identification in peak time, then identify potential peak traffic situation; And
If during potential peak traffic situation, detect at least one peak elevator that still is less than aforementioned simultaneous number, then should potential peak traffic situation be inferred as actual peak traffic situation.
10. one kind is used for entering the computer program of peak traffic situation in elevator device identification, it is characterized in that this computer program comprises the program code that is arranged to carry out following steps:
In the identification in real-time peak time of elevator device, the number of monitoring car call and the car load of admitting the elevator of the passenger in the lobby area;
Determine the car load threshold value,, then elevator is identified as peak elevator if car load surpasses the car load threshold value on its basis;
The threshold value of definition car call is if the threshold value that outnumbers car call of the car call of the floor on its basis outside the lobby area then identifies peak elevator;
In elevator device, collect about passenger's number of arrival floor during the window at the fixed time and leave the ridership purpose statistics of this floor; And
If detect at least one peak elevator and enter peak traffic condition, then select dominant traffic pattern as entering peak traffic condition for the statistics indication that the current time window is collected.
11. computer program according to claim 10 is characterized in that this program code also is arranged to carry out following steps:
Determine the number of the simultaneous peak elevator that the real-time peak traffic situation of identification is required.
12. computer program according to claim 11 is characterized in that this program code also is arranged to carry out following steps:
The number of aforementioned simultaneous peak elevator is chosen as 2.
13. computer program according to claim 10 is characterized in that this program code also is arranged to carry out following steps:
On the basis of statistics and according to passenger's number determine the inlet floor the value of weighing; And
According to the value of determining like this of weighing, during entering peak traffic condition, elevator is directed to the inlet floor.
14. computer program according to claim 10 is characterized in that this program code also is arranged to carry out following steps:
The length of the time window that definition will be used in statistics;
With respect to one day time, calculate the number that in defined time window, arrives and leave the passenger of this floor;
Will at circulating collection between the daytime of being considered, be added on the existing statistics of utilizing the weighting of scheduled update coefficient about the statistics of aforesaid passenger number; And
Infer dominant during each time window, most probable traffic pattern from described statistics.
15., it is characterized in that this program code also is arranged to carry out following steps according to claim 11 or 12 described computer programs:
If described statistics indication peak traffic situation then identifies potential peak traffic situation; And
If the number of detected peak elevator is at least 1 still less than the number of aforementioned simultaneous peak elevator during potential peak traffic situation, think that then this potential peak traffic situation is actual peak traffic situation.
16., it is characterized in that this program code also is arranged to carry out following steps according to claim 11 or 12 described computer programs:
Calculate elevator and leave the described time gap that enters the mouth between the floor;
According to statistics, prediction accumulates in the number of the passenger in the elevator formation during aforementioned time gap;
When the aforementioned passenger who predicts outnumbers the car load threshold value that is used for identification in peak time, identify potential peak traffic situation; And
If the number of detected peak elevator is at least 1 still less than the number of aforementioned simultaneous peak elevator during potential peak traffic situation, infer that then this potential peak traffic situation is actual peak traffic situation.
17., it is characterized in that this program code also is arranged to carry out following steps according to claim 15 or 16 described computer programs:
Be used to discern outside the potential peak traffic situation of actual potential peak traffic situation, needing the peak elevator of described at least simultaneous number.
18. computer program according to claim 16 is characterized in that this program code also is arranged to carry out following steps, wherein:
For before the time window that in statistics, uses and one or more time windows afterwards determine coefficient of weight;
The time window in the moment of considering except being used for, for all aforementioned time windows, by using the number of predicting the passenger who assembles with aforementioned manner of determined coefficient of weight;
If at least one in described passenger's number of predicting surpasses the car load threshold value that is used for identification in peak time, then identify potential peak traffic situation; And
If during potential peak traffic situation, detect at least one peak elevator that still is less than aforementioned simultaneous number, then should potential peak traffic situation be inferred as actual peak traffic situation.
19. one kind is used for entering the system of peak traffic situation in elevator device identification, described system comprises:
At least one elevator (20,23);
Car load weighing-appliance (21,24) is used to calculate the car load of elevator passenger, with the identification peak elevator;
Elevator door optical element (22,25) is used for passenger's number that enters elevator and the passenger's number that leaves elevator are counted;
Control logic (26) is used to distinguish car call with the identification peak elevator, so that control the traffic stream and control elevator device;
It is characterized in that:
This system also comprises: data bank (27), be used for collection of statistical data, and described statistics comprises the number that arrives and leave the passenger of this floor during the window at the fixed time; And be:
Described control logic (26) if be arranged to detects at least one peak elevator and collected statistics indication enters peak traffic condition, thinks that then dominant traffic pattern is to enter peak traffic condition.
20. system according to claim 19 is characterized in that this system also comprises:
Second determines parts (26), is used for determining the number of simultaneous peak elevator, and this number is required for the real-time peak traffic situation of identification.
21. system according to claim 20 is characterized in that this system also comprises:
Finder (26) is used for the number of described simultaneous peak elevator is chosen as 2.
22. system according to claim 19 is characterized in that this system also comprises:
First determines parts (26), be used in statistics the basis, according to passenger's number determine to enter the mouth value of weighing of floor; And
Function unit (26) is used for the value of weighing determined according to like this, and elevator is directed to the inlet floor during entering peak traffic condition.
23. system according to claim 19 is characterized in that this system also comprises:
The 3rd determines parts (26), is used for determining the length of the time window that will use in statistics;
Calculating unit (26) is used for the time with respect to one day, calculates the number that arrives and leave the passenger of this floor in defined time window;
Summation component (26), be used for by will at circulating collection between the daytime of being considered and comprise that the described statistics of ridership purpose is added to the existing statistics (27) of utilizing the weighting of scheduled update coefficient; And
The first derivation parts (26) are used for deriving dominant most probable traffic pattern during each time window according to described statistics.
24., it is characterized in that this system also comprises according to claim 20 or 21 described systems:
First identification component (26) if be used for aforesaid statistical data indication peak traffic situation, then identifies potential peak traffic situation; And
The second derivation parts (26), be at least 1 still less than the number of aforementioned simultaneous peak elevator if be used for the number of detected peak elevator during potential peak traffic situation, think that then this potential peak traffic situation is actual peak traffic condition.
25., it is characterized in that this system also comprises according to claim 20 or 21 described systems:
Time gap is determined parts (26), is used to calculate elevator and leaves the average time interval that enters the mouth between the floor;
Estimation section (26) is used for predicting the number that accumulates in the passenger of elevator formation during aforementioned time gap according to statistics;
First identification component (26) is used for identifying potential peak traffic situation when the aforementioned passenger who predicts outnumbers the car load threshold value that is used for identification in peak time; And
The second derivation parts (26), be at least 1 still less than the number of aforementioned simultaneous peak elevator if be used for the number of detected peak elevator during potential peak traffic situation, infer that then this potential peak traffic situation is actual peak traffic situation.
26. according to claim 24 or 25 described systems, it is characterized in that: the described second derivation parts (26) are arranged to: be used to discern outside the potential peak traffic situation of actual peak traffic situation, needing the peak elevator of aforementioned at least number.
27. system according to claim 25 is characterized in that this system also comprises:
The 4th definite parts (26) are used to before the time window that uses in statistics and one or more time windows are afterwards determined coefficient of weight;
Estimation section (26) is the time window in the moment that is used for considering except being used for, for all aforementioned time windows, by using the number of determined coefficient of weight with the passenger of aforementioned manner forecasting institute accumulation;
Second identification component (26) if at least one that is used for aforementioned passenger's number of predicting surpasses the car load threshold value be used for identification in peak time, then identifies potential peak traffic situation; And
The second derivation parts (26) if the number of detected peak elevator is at least 1 still less than the number of aforementioned simultaneous peak elevator during potential peak traffic situation, infer that then this potential peak traffic situation is actual peak traffic situation.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FI20030972A FI113531B (en) | 2003-06-30 | 2003-06-30 | Detection of an input congestion |
| FI20030972 | 2003-06-30 | ||
| PCT/FI2004/000232 WO2005000726A1 (en) | 2003-06-30 | 2004-04-15 | Identification of incoming peak traffic for elevators |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1795132A true CN1795132A (en) | 2006-06-28 |
| CN1795132B CN1795132B (en) | 2011-02-09 |
Family
ID=8566323
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN200480014443.0A Expired - Lifetime CN1795132B (en) | 2003-06-30 | 2004-04-15 | Identification of incoming peak traffic for elevators |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US7735611B2 (en) |
| EP (1) | EP1638879B1 (en) |
| CN (1) | CN1795132B (en) |
| FI (1) | FI113531B (en) |
| HK (1) | HK1092772A1 (en) |
| WO (1) | WO2005000726A1 (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102398804A (en) * | 2011-07-12 | 2012-04-04 | 江苏镇安电力设备有限公司 | Elevator group control dispatching method |
| CN103010877A (en) * | 2011-09-20 | 2013-04-03 | 株式会社日立制作所 | Energy-saving elevator |
| CN103332542A (en) * | 2013-05-16 | 2013-10-02 | 上海永大电梯设备有限公司 | Passenger flow peak perception method of elevator group control system and self-adaption elevator dispatching method |
| CN104030108A (en) * | 2013-03-08 | 2014-09-10 | 株式会社东芝 | Elevator traffic need prediction device |
| CN109230917A (en) * | 2017-07-11 | 2019-01-18 | 奥的斯电梯公司 | Identification and seamless call elevator to the crowd in elevator waiting area |
| CN109455588A (en) * | 2018-12-26 | 2019-03-12 | 住友富士电梯有限公司 | A kind of control method of double-car elevator, control system and lift facility |
| CN113697619A (en) * | 2021-08-31 | 2021-11-26 | 武汉理工大学 | Passenger flow staged elevator handling group control dispatching method based on ant colony algorithm |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7921968B2 (en) * | 2005-03-18 | 2011-04-12 | Otis Elevator Company | Elevator traffic control including destination grouping |
| FI118381B (en) | 2006-06-19 | 2007-10-31 | Kone Corp | Elevator system |
| CA2773909C (en) * | 2009-09-11 | 2016-11-15 | Inventio Ag | Method for operating an elevator system |
| DE102009049267A1 (en) * | 2009-10-13 | 2011-04-21 | K-Solutions Gmbh | Method for controlling a lift and a lift group |
| FI122222B (en) * | 2009-12-22 | 2011-10-14 | Kone Corp | Elevator system |
| CN103068709B (en) * | 2010-08-19 | 2016-05-04 | 通力股份公司 | Passenger flows management system |
| WO2012042095A1 (en) | 2010-09-30 | 2012-04-05 | Kone Corporation | Elevator system |
| CN103764533B (en) * | 2011-09-08 | 2017-05-31 | 奥的斯电梯公司 | Elevator system with dynamic transport distribution solution |
| CN107683251A (en) * | 2015-06-05 | 2018-02-09 | 通力股份公司 | Method for calling distribution in eleva-tor bank |
| EP3505473A1 (en) * | 2018-01-02 | 2019-07-03 | KONE Corporation | Forecasting elevator passenger traffic |
| EP3560870A3 (en) | 2018-04-24 | 2019-11-20 | Otis Elevator Company | Automatic cognitive analysis of elevators to reduce passenger wait time |
| EP3628619A1 (en) * | 2018-09-27 | 2020-04-01 | Otis Elevator Company | Elevator system |
| CN110817621A (en) * | 2019-11-14 | 2020-02-21 | 深圳市万物云科技有限公司 | Building passenger lift peak dispatching method, device, computer equipment and storage medium |
| CN113071962A (en) * | 2021-04-20 | 2021-07-06 | 江苏省特种设备安全监督检验研究院 | Control method and control device for early-peak direct-falling elevator and elevator adopting method |
| CN113800342A (en) * | 2021-09-14 | 2021-12-17 | 曹琛 | Efficient self-adaptive elevator control method |
| CN113896063B (en) * | 2021-10-25 | 2023-06-27 | 嘉兴科纳沃川电子有限公司 | Intelligent elevator dispatching system based on AI intelligence |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3967702A (en) * | 1973-12-19 | 1976-07-06 | Hitachi, Ltd. | Control apparatus for elevators |
| US3973649A (en) * | 1974-01-30 | 1976-08-10 | Hitachi, Ltd. | Elevator control apparatus |
| JPS5197155A (en) * | 1975-02-21 | 1976-08-26 | Erebeetano jokyakudeetashushusochi | |
| FI91238C (en) | 1989-11-15 | 1994-06-10 | Kone Oy | Control procedure for elevator group |
| US5276295A (en) * | 1990-09-11 | 1994-01-04 | Nader Kameli | Predictor elevator for traffic during peak conditions |
| JPH04246077A (en) * | 1990-09-11 | 1992-09-02 | Otis Elevator Co | Floor population detecting device for elevator control device |
| US5260526A (en) * | 1991-04-29 | 1993-11-09 | Otis Elevator Company | Elevator car assignment conditioned on minimum criteria |
| US5260527A (en) * | 1991-04-29 | 1993-11-09 | Otis Elevator Company | Using fuzzy logic to determine the number of passengers in an elevator car |
| AU645882B2 (en) | 1991-04-29 | 1994-01-27 | Otis Elevator Company | Using fuzzy logic to determine the number of passengers in an elevator car |
| US5274202A (en) * | 1992-08-10 | 1993-12-28 | Otis Elevator Company | Elevator dispatching accommodating interfloor traffic and employing a variable number of elevator cars in up-peak |
| JP3414846B2 (en) * | 1993-07-27 | 2003-06-09 | 三菱電機株式会社 | Transportation control device |
| KR960011574B1 (en) * | 1994-02-08 | 1996-08-24 | 엘지산전 주식회사 | Elevator group control method and device |
| FI111929B (en) * | 1997-01-23 | 2003-10-15 | Kone Corp | Operation of a lift group |
| WO1999036340A1 (en) * | 1998-01-19 | 1999-07-22 | Mitsubishi Denki Kabushiki Kaisha | Management controller of elevators |
| ATE552199T1 (en) * | 2007-08-28 | 2012-04-15 | Thyssenkrupp Elevator Capital Corp | OVERLOAD CONTROL FOR DESTINATION DISPATCH SYSTEMS |
-
2003
- 2003-06-30 FI FI20030972A patent/FI113531B/en not_active IP Right Cessation
-
2004
- 2004-04-15 CN CN200480014443.0A patent/CN1795132B/en not_active Expired - Lifetime
- 2004-04-15 EP EP04727578.9A patent/EP1638879B1/en not_active Expired - Lifetime
- 2004-04-15 US US10/553,054 patent/US7735611B2/en active Active
- 2004-04-15 WO PCT/FI2004/000232 patent/WO2005000726A1/en active Application Filing
-
2006
- 2006-12-06 HK HK06113409.2A patent/HK1092772A1/en not_active IP Right Cessation
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102398804A (en) * | 2011-07-12 | 2012-04-04 | 江苏镇安电力设备有限公司 | Elevator group control dispatching method |
| CN103010877A (en) * | 2011-09-20 | 2013-04-03 | 株式会社日立制作所 | Energy-saving elevator |
| CN103010877B (en) * | 2011-09-20 | 2015-07-08 | 株式会社日立制作所 | Energy-saving elevator |
| CN104030108A (en) * | 2013-03-08 | 2014-09-10 | 株式会社东芝 | Elevator traffic need prediction device |
| CN104030108B (en) * | 2013-03-08 | 2017-01-04 | 株式会社东芝 | Elevator traffic needs prediction means |
| CN103332542A (en) * | 2013-05-16 | 2013-10-02 | 上海永大电梯设备有限公司 | Passenger flow peak perception method of elevator group control system and self-adaption elevator dispatching method |
| CN109230917A (en) * | 2017-07-11 | 2019-01-18 | 奥的斯电梯公司 | Identification and seamless call elevator to the crowd in elevator waiting area |
| US10676315B2 (en) | 2017-07-11 | 2020-06-09 | Otis Elevator Company | Identification of a crowd in an elevator waiting area and seamless call elevators |
| CN109455588A (en) * | 2018-12-26 | 2019-03-12 | 住友富士电梯有限公司 | A kind of control method of double-car elevator, control system and lift facility |
| CN109455588B (en) * | 2018-12-26 | 2021-08-10 | 住友富士电梯有限公司 | Control method and control system of double-car elevator and elevator equipment |
| CN113697619A (en) * | 2021-08-31 | 2021-11-26 | 武汉理工大学 | Passenger flow staged elevator handling group control dispatching method based on ant colony algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| FI113531B (en) | 2004-05-14 |
| EP1638879A1 (en) | 2006-03-29 |
| US20070084674A1 (en) | 2007-04-19 |
| EP1638879B1 (en) | 2014-05-21 |
| HK1092772A1 (en) | 2007-02-16 |
| WO2005000726A1 (en) | 2005-01-06 |
| FI20030972A0 (en) | 2003-06-30 |
| CN1795132B (en) | 2011-02-09 |
| US7735611B2 (en) | 2010-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1795132B (en) | Identification of incoming peak traffic for elevators | |
| EP0544540B1 (en) | Elevator system having improved crowd service based on empty car assignment | |
| US7849974B2 (en) | Method of dispatching an elevator car | |
| US8104585B2 (en) | Method of assigning hall calls based on time thresholds | |
| JP2935854B2 (en) | Elevator control device and elevator control method | |
| US4672531A (en) | Elevator supervisory learning control apparatus | |
| Siikonen | Elevator group control with artificial intelligence | |
| CN1208229C (en) | Elevator group control device | |
| US5183981A (en) | "Up-peak" elevator channeling system with optimized preferential service to high intensity traffic floors | |
| CN1011300B (en) | Elevator group controller for immediate allocation of destination floor calls | |
| CN111847149A (en) | Elevator destination floor allocation method | |
| JP5082215B2 (en) | Elevator group management apparatus and group management method | |
| CN116234765B (en) | Monitoring system for elevator | |
| CN116323456B (en) | Monitoring system for elevator | |
| KR100237616B1 (en) | Method and apparatus for controlling a plurality of elevator cars | |
| KR960013089B1 (en) | Elevator group control method | |
| WO2023104290A1 (en) | Management of passenger flow with elevator system | |
| JP5375926B2 (en) | Elevator group management device | |
| KR19980073216A (en) | Military management system of elevator and allocation method of elevator using the same | |
| CN112010123A (en) | Elevator distribution device | |
| Thangavelu et al. | Artificial intelligence", based learning system predicting" peak-period" ti |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 1092772 Country of ref document: HK |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: GR Ref document number: 1092772 Country of ref document: HK |
|
| CX01 | Expiry of patent term | ||
| CX01 | Expiry of patent term |
Granted publication date: 20110209 |